{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3a79ef54-63e6-429f-ba9f-590b736a7f29",
   "metadata": {},
   "source": [
    "# 4.4 在三维空间中绘制平面图"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c90a18b9-d8e8-431c-856c-35f1e4ed05e1",
   "metadata": {},
   "source": [
    "### 1.任务描述\n",
    "\n",
    "一组房屋面积的数据如下（16个）:\n",
    "\n",
    "137.97,104.50,100.00,124.32,79.20,99.00,124.00,114.00,106.69,138.05,53.75,46.91,68.00,63.02,81.26,86.21。\n",
    "\n",
    "与房屋面积对应的房间数为：\n",
    "\n",
    "3,2,2,3,1,2,3,2,2,3,1,1,1,1,2,2。\n",
    "\n",
    "假设房屋价格与房屋面积和房间数之间是线性关系，那么可以通过如下公式求得房屋价格：\n",
    "\n",
    "$y=w_0+w_1x_1+w_2x_2$\n",
    "\n",
    "式中，$x_1$为房屋面积，$x_2$为房间数，y为房屋价格，w_0=11.96729093，w_1=0.53488599，w_2=14.33150378。\n",
    "\n",
    "要求：\n",
    "\n",
    "- 根据房屋价格的公式，求得上述房屋的价格y_pred\n",
    "- 以面积x1为x轴，以房间数x2为y轴，以价格y_pred为z轴，绘制平面图"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8d7baa9c-93a2-42f3-a3c1-231cdb587f2d",
   "metadata": {},
   "source": [
    "### 2.知识准备\n",
    "\n",
    "见教程。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74ad989a-9b82-43e1-b841-e74284cd5936",
   "metadata": {},
   "source": [
    "### 3.任务分析\n",
    "\n",
    "可以使用Axes3D.plot_surface方法在三维坐标中绘制平面图。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "435c6090-cfda-4f46-a550-22a368e41e4a",
   "metadata": {},
   "source": [
    "### 4.任务实施\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ec75eb6c-5da3-467d-a471-ca3b47242dd6",
   "metadata": {},
   "source": [
    "执行代码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2ae9da58-e339-4d22-9f8d-ca255711d89e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_10524\\45760886.py:12: MatplotlibDeprecationWarning: Axes3D(fig) adding itself to the figure is deprecated since 3.4. Pass the keyword argument auto_add_to_figure=False and use fig.add_axes(ax) to suppress this warning. The default value of auto_add_to_figure will change to False in mpl3.5 and True values will no longer work in 3.6.  This is consistent with other Axes classes.\n",
      "  ax3d=Axes3D(fig)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf  \n",
    "import matplotlib.pyplot as plt  \n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "from mpl_toolkits.mplot3d import Axes3D  \n",
    "# 房屋面积\n",
    "x1=tf.constant([137.97,104.50,100.00,124.32,79.20,99.00,124.00,114.00,106.69,138.05,53.75,46.91,68.00,63.02,81.26,86.21],dtype=tf.float64)  \n",
    "# 房间数\n",
    "x2=tf.constant([3,2,2,3,1,2,3,2,2,3,1,1,1,1,2,2],dtype=tf.float64) \n",
    "# 创建画布\n",
    "fig=plt.figure(figsize=(8,6))  \n",
    "# 创建3D工具\n",
    "ax3d=Axes3D(fig)\n",
    "# 设置x,y,z轴的名称\n",
    "ax3d.set_xlabel('面积',color='r',fontsize=16)  \t\n",
    "ax3d.set_ylabel('房间数',color='r',fontsize=16)  \n",
    "ax3d.set_zlabel('价格',color='r',fontsize=16)  \n",
    "ax3d.set_yticks([1,2,3])\n",
    "ax3d.set_zlim3d(30,160)  \n",
    "# 改变视角  \n",
    "# ax3d.view_init(elev=0,azim=90)  \n",
    "ax3d.view_init(elev=0,azim=45)  \n",
    "# 绘制平面  \n",
    "# 创建网格点坐标矩阵\n",
    "X1,X2=tf.meshgrid(x1,x2)\n",
    "# 根据公式求房屋价格\n",
    "w0=tf.constant([11.96729093],dtype=tf.float64)\n",
    "w1=tf.constant([0.53488599],dtype=tf.float64)\n",
    "w2=tf.constant([14.33150378],dtype=tf.float64)\n",
    "y_pred=w0+w1*X1+w2*X2\n",
    "# 绘制平面图 \n",
    "ax3d.plot_surface(X1,X2,y_pred,cmap=\"coolwarm\")  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f2c61db-4b69-49f9-9b22-f6d2642a056d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
